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RE: Why should we avoid using micro rate constants?

Date: Tue, 5 Feb 2019 10:53:21 +0000

Dear Sumeet,

If you are assuming a distribution for your parameters (e.g. log-Normal p =
= theta * exp(eta)) then it might matter if you use rate constants versus=
 clearances and volumes. In general, if you want to make the log-Normal as=
sumption you should use clearances and volumes as there is reasonable biolo=
gical prior knowledge to show these generally follow a log-Normal distribut=
ion (do some reading on the occurrence of log-Normal distributions in biolo=

The rate constant is a ratio of two (usually) log-Normally distributed vari=
ables (e.g. k = CL/V) and hence may not necessarily be a shape that can i=
tself be described as a log-Normal. Here is some R-code that highlights th=

# Simulate some realistic PK for a water soluble renally cleared drug
vd <- 40 * exp(rnorm(10000, sd = 0.5))
cl <- 6 * exp(rnorm(10000, sd = 0.5))
k <- cl / vd
# Visualise the histograms and use fitdistr function to
# fit a log-Normal
# Volume:
hist(vd, freq = FALSE)
fit <- fitdistr(vd, "log-normal")$estimate
lines(dlnorm(0:max(vd), fit[1], fit[2]), lwd = 3)
# ...yes
# Clearance:
hist(cl, freq = FALSE)
fit <- fitdistr(cl, "log-normal")$estimate
lines(dlnorm(0:max(cl), fit[1], fit[2]), lwd = 3)
# ...yes
# K
hist(k, freq = FALSE)
fit <- fitdistr(k, "log-normal")$estimate
lines(dlnorm(0:max(k), fit[1], fit[2]), lwd = 3)

People who do not like to make assumptions on distributions of parameters u=
se a nonparametric approach, and in this case it does not matter whether yo=
u use rate constants or clearances and volumes. However, unless you collec=
t rich informative data (to get good individual parameter estimates) and lo=
ts of it (to get a true idea of the distribution of parameters in the popul=
ation) it is usually advised to make a distributional assumption, and the l=
og-Normal is often sensible.



Joseph F Standing
MRC Fellow, UCL Institute of Child Health
Antimicrobial Pharmacist, Great Ormond Street Hospital
Honorary Senior Lecturer, St George's University of London
Tel: +44(0)207 905 2370
Mobile: +44(0)7970 572435
From: owner-nmusers
 of janet.wade
Sent: 05 February 2019 06:51
To: 'Leonid Gibiansky'; 'Singla, Sumeet K'
Cc: nmusers
Subject: RE: [NMusers] Why should we avoid using micro rate constants?

Hi All,

It could also be the statistical model. If you are estimating 4 parameters =
then different parameterisations should be fairly equivalent if a BLOCK(4) =
structure is used for both parameterisations. If only the diagonal option i=
s used, then this could be why different results/minimisations are obtained=
 for different parameterisations.

Kind regards,

Janet R Wade, PhD
Senior Consultant

From: owner-nmusers
 Of Leonid Gibiansky
Sent: 04 February 2019 07:30
To: Singla, Sumeet K <sumeet-singla
Cc: nmusers
Subject: Re: [NMusers] Why should we avoid using micro rate constants?

It could be just coding error, could you show the control stream?

On Feb 3, 2019, at 12:44 PM, Singla, Sumeet K <sumeet-singla
Hello everyone!

I have a question. I was trying to build a 2-compartment PK model for marij=
uana use in occasional and chronic smokers. Initially, I was using providin=
g rate constants K12 and K21 ­in PK block and it resulted in poor fitting=
. Then, I later changed to CL,V1, V2 , Q and it resulted in proper fitting.=
 I was perplexed as to why I couldn’t get a proper fit by providing rate =
constants? I tried to look online but couldn’t find any proper explanatio=
n about when (or not) we should use micro constants in PK block to define o=
ur model in NONMEM? Does anyone has any useful insights into this?

Sumeet Singla
Graduate Student
Dpt. of Pharmaceutics & Translational Therapeutics
College of Pharmacy- University of Iowa


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Received on Tue Feb 05 2019 - 05:53:21 EST

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